frontrun_enriched_follows

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'full enrichment' and merging of AI classification, custom rules, and custom tags, which adds some context about what the tool does beyond basic retrieval. However, it doesn't disclose critical behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what happens if no data is found. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded: the first sentence states the core purpose and key features, and the second emphasizes its power for workflows. There's no wasted text, and it efficiently conveys value. However, it could be slightly more structured by explicitly separating features from usage advice, but it's still highly concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (enrichment tool with no annotations and no output schema), the description is moderately complete. It covers the purpose and enrichment aspects but lacks details on output format, error handling, or behavioral constraints. Without annotations or output schema, more context on what the tool returns or its limitations would be beneficial. It's adequate for a basic understanding but has clear gaps for a tool involving custom rules and tags.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents the two parameters ('since' and 'username') with descriptions. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors beyond the schema's 'Default: "24h"', or how enrichment applies to filtered results. With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get new follows with full enrichment' which specifies the action (get) and resource (new follows). It distinguishes itself by mentioning 'AI classification + your custom rules + your custom tags, all merged' and calls it 'the most powerful endpoint for custom workflows,' which helps differentiate it from siblings like 'frontrun_new_follows' that likely provide basic follows without enrichment. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by stating 'This is the most powerful endpoint for custom workflows,' suggesting it should be used for advanced scenarios. It doesn't provide explicit when-to-use vs. when-not-to-use guidance or name specific alternatives like 'frontrun_new_follows' for basic follows. The implication is adequate but lacks clear exclusions or direct sibling comparisons, placing it at a medium level.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.